Search Results for author:
Found 6 papers, 3 papers with code
Thresholding Classifiers to Maximize F1 Score
As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive.
Temporal-Clustering Invariance in Irregular Healthcare Time Series
We postulate that fine temporal detail, e. g., whether a series of blood tests are completed at once or in rapid succession should not alter predictions based on this data.
Mixture Proportion Estimation and PU Learning:A Modern Approach
Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier.
Off-Policy Risk Assessment in Markov Decision Processes
To mitigate these problems, we incorporate model-based estimation to develop the first doubly robust (DR) estimator for the CDF of returns in MDPs.
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift
Self-training and contrastive learning have emerged as leading techniques for incorporating unlabeled data, both under distribution shift (unsupervised domain adaptation) and when it is absent (semi-supervised learning).
Post-Hoc Reversal: Are We Selecting Models Prematurely?
This phenomenon is especially prominent in high-noise settings.
